Article ; Online: Protein Design Using Structure-Prediction Networks: AlphaFold and RoseTTAFold as Protein Structure Foundation Models.
Cold Spring Harbor perspectives in biology
2024
Abstract: Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design ... ...
| Abstract | Designing proteins with tailored structures and functions is a long-standing goal in bioengineering. Recently, deep learning advances have enabled protein structure prediction at near-experimental accuracy, which has catalyzed progress in protein design as well. We review recent studies that use structure-prediction neural networks to design proteins, via approaches such as activation maximization, inpainting, or denoising diffusion. These methods have led to major improvements over previous methods in wet-lab success rates for designing protein binders, metalloproteins, enzymes, and oligomeric assemblies. These results show that structure-prediction models are a powerful foundation for developing protein-design tools and suggest that continued improvement of their accuracy and generality will be key to unlocking the full potential of protein design. |
|---|---|
| Language | English |
| Publishing date | 2024-03-04 |
| Publishing country | United States |
| Document type | Journal Article |
| ISSN | 1943-0264 |
| ISSN (online) | 1943-0264 |
| DOI | 10.1101/cshperspect.a041472 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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